تخطيط وكلاء LLM: هل يحسّن التفكيك الهرمي (Hierarchical Decomposition) من نجاح مهام الويب؟

الادعاءات والمراجعة

الادعاءات والمراجعة

1. resultمدعوم

   Structured Planning Domain Definition Language (PDDL) plans produce more concise and goal-directed strategies than natural language (NL) plans.

   [1] Why Do LLM-based Web Agents Fail? A Hierarchical Planning Perspective — abstract arXiv:2603.14248v2

   “Large language model (LLM) web agents are increasingly used for web navigation but remain far from human reliability on realistic, long-horizon tasks. Existing evaluations focus primarily on end-to-end success, offering limited insight into where failures arise. We propose a hier…”

2. resultمدعوم

   Low-level execution remains the dominant bottleneck in LLM-based web agents.

   [1] Why Do LLM-based Web Agents Fail? A Hierarchical Planning Perspective — abstract arXiv:2603.14248v2

   “Large language model (LLM) web agents are increasingly used for web navigation but remain far from human reliability on realistic, long-horizon tasks. Existing evaluations focus primarily on end-to-end success, offering limited insight into where failures arise. We propose a hier…”

3. methodمدعوم

   The hierarchical planning framework analyzes web agents across three layers: high-level planning, low-level execution, and replanning.

   [1] Why Do LLM-based Web Agents Fail? A Hierarchical Planning Perspective — abstract arXiv:2603.14248v2

   “Large language model (LLM) web agents are increasingly used for web navigation but remain far from human reliability on realistic, long-horizon tasks. Existing evaluations focus primarily on end-to-end success, offering limited insight into where failures arise. We propose a hier…”

4. limitationمدعوم

   Existing evaluations focus primarily on end-to-end success, offering limited insight into where failures arise.

   [1] Why Do LLM-based Web Agents Fail? A Hierarchical Planning Perspective — abstract arXiv:2603.14248v2

   “Large language model (LLM) web agents are increasingly used for web navigation but remain far from human reliability on realistic, long-horizon tasks. Existing evaluations focus primarily on end-to-end success, offering limited insight into where failures arise. We propose a hier…”

5. methodمدعوم

   The CoAct framework involves a global planning agent and a local execution agent.

   [2] CoAct: A Global-Local Hierarchy for Autonomous Agent Collaboration — abstract S2 e93f1fdaecd6

   “Existing LLMs exhibit remarkable performance on various NLP tasks, but still struggle with complex real-world tasks, even equipped with advanced strategies like CoT and ReAct. In this work, we propose the CoAct framework, which transfers the hierarchical planning and collaboratio…”

6. resultمدعوم

   CoAct achieves superior performance over baseline methods on long-horizon web tasks on the WebArena benchmark.

   [2] CoAct: A Global-Local Hierarchy for Autonomous Agent Collaboration — abstract S2 e93f1fdaecd6

   “Existing LLMs exhibit remarkable performance on various NLP tasks, but still struggle with complex real-world tasks, even equipped with advanced strategies like CoT and ReAct. In this work, we propose the CoAct framework, which transfers the hierarchical planning and collaboratio…”

7. resultمدعوم

   PageDigest substantially reduces observation length while improving overall task success rate across diverse backbone LLMs and established agent methods on the WebArena benchmark.

   [3] Region4Web: Rethinking Observation Space Granularity for Web Agents — abstract S2 520977177230

   “Web agents perceive web pages through an observation space, yet its granularity has remained an underexamined design choice. Existing work treats observation at the same element-level granularity as the action space, leaving the page's functional organization implicit and forcing…”

8. methodمدعوم

   WebAgent plans ahead by decomposing instructions into canonical sub-instructions, summarizes long HTML documents into task-relevant snippets, and acts on websites via Python programs.

   [4] A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis — abstract DOI 10.48550/arxiv.2307.12856

   “Pre-trained large language models (LLMs) have recently achieved better generalization and sample efficiency in autonomous web automation. However, the performance on real-world websites has still suffered from (1) open domainness, (2) limited context length, and (3) lack of induc…”

9. resultمدعوم

   WebAgent improves the success on real websites by over 50%.

   [4] A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis — abstract DOI 10.48550/arxiv.2307.12856

   “Pre-trained large language models (LLMs) have recently achieved better generalization and sample efficiency in autonomous web automation. However, the performance on real-world websites has still suffered from (1) open domainness, (2) limited context length, and (3) lack of induc…”

10. resultمدعوم

    SkillWeaver achieves relative success rate improvements of 31.8% on WebArena and 39.8% on real-world websites.

    [5] SkillWeaver: Web Agents can Self-Improve by Discovering and Honing Skills — abstract S2 69768fdcb8c2

    “To survive and thrive in complex environments, humans have evolved sophisticated self-improvement mechanisms through environment exploration, hierarchical abstraction of experiences into reuseable skills, and collaborative construction of an ever-growing skill repertoire. Despite…”

11. resultمدعوم

    AdaPlanner outperforms state-of-the-art baselines by 3.73% on ALFWorld and 4.11% on MiniWoB++ while utilizing 2x and 600x fewer samples respectively.

    [6] AdaPlanner: Adaptive Planning from Feedback with Language Models — abstract DOI 10.48550/arxiv.2305.16653

    “Large language models (LLMs) have recently demonstrated the potential in acting as autonomous agents for sequential decision-making tasks. However, most existing methods either take actions greedily without planning or rely on static plans that are not adaptable to environmental …”

12. factمدعوم

    TREK comprises 800 multi-constraint tasks over a synthetic knowledge base of 212,530 records across 375 cities and 13 personas.

    [7] TREK: A Travel Reasoning and Evaluation Kit for LLM Agents in Complex Trip Planning — abstract arXiv:2607.26977v2

    “Travel planning is a demanding stress test for tool-using LLM agents: a usable itinerary is a single artifact that must be right along many axes at once - every flight, hotel, and attraction must exist and be bookable, the days must be physically traversable, the total must clear…”

13. methodمدعوم

    Every task in TREK is scored by a fully deterministic, rule-based evaluator with no LLM judge.

    [7] TREK: A Travel Reasoning and Evaluation Kit for LLM Agents in Complex Trip Planning — abstract arXiv:2607.26977v2

    “Travel planning is a demanding stress test for tool-using LLM agents: a usable itinerary is a single artifact that must be right along many axes at once - every flight, hotel, and attraction must exist and be bookable, the days must be physically traversable, the total must clear…”

14. limitationمدعوم

    The latency for generating a plan with an LLM can reach tens of seconds, significantly degrading user experience.

    [9] A Plan Reuse Mechanism for LLM-Driven Agent — abstract arXiv:2512.21309v2

    “Integrating large language models (LLMs) into personal assistants, like Xiao Ai and Blue Heart V, effectively enhances their ability to interact with humans, solve complex tasks, and manage IoT devices. Such assistants are also termed LLM-driven agents. Upon receiving user reques…”